Gowtham Dhanarasi et al. / International Journal of Engineering Science and Technology (IJEST)
IMAGE STEGANOGRAPHY USING BLOCK COMPLEXITY ANALYSIS GOWTHAM DHANARASI1 Department of ECE, JNTU Kakinada Kakinada, Andhra Pradesh, 533003, INDIA
[email protected]
Dr.A. Mallikarjuna Prasad2 Department of ECE, JNTU Kakinada Kakinada, Andhra Pradesh, 533003, INDIA
[email protected] Abstract: A block complexity analysis for transform domain image stegonagraphy is introduced in this paper. The algorithm proposed here works on the wavelet transform coefficients which embedded the secret data into the original image. The technique implemented which are capable of producing a secret-embedded image that is indistinguishable from the original image to human eye. This can be achieved by retaining integrity of the wavelet coefficients at high capacity embedding. This improvement to capacity-quality trading –off interrelation is analyzed in detailed and experimentally illustrated in the paper. Keywords: stegonagraphy ; capacity-quality trading –off interrelation;. 1. Introduction Digital data communication is an essential part of everyone’s life. Data communications have some problems such as internet security, copyright protection etc. To avoid these problems, cryptography is one of the methods. However, encryption results in a disordered and confusing message and can attract eavesdroppers easily. Steganography methods overcome this problem by hiding the secret information behind a cover media (video, audio or image) because the presence of information cannot be noticed by any attacker. Using steganography, embed a secret message inside a piece of unsuspicious information and send it without anyone knowing of the existence of the secret message. Steganography and cryptography are closely related. Cryptography scrambles messages so they cannot be understood. Steganography on the other hand, will hide the message so there is no knowledge of the existence of the message in the first place. In some situations, sending an encrypted message will arouse suspicion while an “invisible” message will not do so. Both sciences can be combined to produce better protection of the message. In this case, when the steganography fails and the message can be detected, it is still of no use as it is encrypted using cryptography techniques. Steganographic techniques have been used for centuries. The first known application dates back to the ancient Greek times, when messenger stat toed messages on their s halved heads and then let their hair grow so the message remained unseen. A deferent method from that time used wax tables as a cover source. Text was written on the underlying wood and the message was covered with a new wax layer. The tablets appeared to be blank so they passed inspection without question. In the 20th century, invisible inks where a widely used technique. In the Second World War, people used milk, vinegar, fruit juices and urine to write secret messages. When heated, these fluids become darker and the message could be read. Even later, the Germans developed a technique called the microdot. Micro dots are photographs with the size of a printed period but have the clarity of a standard type-written page. The micro dots where then printed in a letter or on an envelope and being so small, they could be sent unnoticed. 2. Proposed Algorithm This algorithm is based on wavelet transform and bit plane complexity segmentation. 2.1.Wavelet transform The proposed scheme uses the wavelet transform presentation of the cover image to conceal the secret message. In a four-band two-dimensional wavelet transform, the LL band includes the low pass coefficients and
ISSN : 0975-5462
Vol. 4 No.07 July 2012
3439
Gowtham Dhanarasi et al. / International Journal of Engineering Science and Technology (IJEST)
represents a soft approximation to the image. The HL, LH and HH bands represent the vertical, horizontal, and diagonal features of the image, respectively. These three bands convey the details of the image. We can do the same decomposition on the LL quadrant up to log2 (min (height, width)). Figure 1 visualizes a two-level wavelet transform. The 2D wavelet transform used in this algorithm is the integer wavelet transform introduced in [4], the same transform used in [5].
Fig. 1.Two Dimensional wavelet transform
2.2.Bit Plane Complexity Segmentation Generally, wavelet domain allows for hiding data in regions that the HVS is less sensitive [1]. To do this, we adapt the amount of embedded data in each region of wavelet transform domain with a measure of noisiness in that region. Here, we use the bit-plane complexity segmentation (BPCS) [2] as the measure of noisiness. Each rgb component of a 24-bit bitmap image is an 8-bit value that changes from 0 to 255. In each color plane, the value zero represents the darkest shade of that color, where the brightest shading corresponds to the 255 value. Fig. 2 shows a 4 x 4 test image with the rgb values shown in table i. therefore, the r channel is decomposed as indicated in table ii. now, the bit plane segmentation, visualized in figure 3, results in eight binary planes for r channel, as shown in table III. As a benchmark to measure the amount of noisiness of a bit plane, we use the black and white border image complexity defined by Kawaguchi [2]. Based on the definition, the complexity for a black and white border P (equivalent to our segmented plane) is the ratio of the number of total B-W changes in the plane to its maximum possible value, denoted as α(P), where 0
